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Reliable testing of a mechanical system requires the procedures used for the evaluation to be repeatable and reproducible. However, it is never possible to exactly repeat or reproduce the tests that are used for evaluation. To overcome this limitation, a statistical evaluation procedure can generally be used. However, most of the statistical procedures use scalar values as input without the ability to handle vectors or time-histories. To overcome these limitations, two numerical/statistical methods for determining if the impact time-history response of a mechanical system is repeatable or reproducible are evaluated and elaborated upon. Such a system could be a vehicle, a biological human surrogate, an Anthropometric Test Device (ATD or dummy), etc. The responses could be sets of time-histories of accelerations, forces, moments, etc., of a component or of the system. The example system evaluated is the BioRID II rear impact dummy.

A comparison of the NHTSA advanced dummy and the Hybrid III is presented in this paper based on their performance in twenty four frontal impact sled tests. Various time histories pertaining to accelerations, angular velocities, deflections and forces have been compared between the two dummies in light of their design differences. This has lead to some understanding about the differences and similarities between the NHTSA advanced dummy and the Hybrid III. In general, the chest as well as the head motion in the NHTSA advanced dummy are greater. The lumbar moments in the NHTSA advanced dummy are lower than that in the Hybrid III. The upper and lower spine segments in the NHTSA advanced dummy, generally rotate more than the spine of the Hybrid III.

Head-to-knee interaction of the right front passenger dummy can occur in some 30-35 MPH crash barrier tests. The biofidelity and significance of these interactions as related to predicting human response was addressed in this study. In a series of laboratory experiments an instrumented headform was dropped on the dummy knee to simulate the barrier interactions. These test results were then related to the human by dropping the same headform on the cadaver leg. The instrumented headform was dropped from three heights to impact the Part 572 dummy knee at three velocities. Two impact sites and two impact angles were used. These test parameters bracketed the barrier conditions. Measurements from headform accelerometers permitted calculation of HIC value for comparison to barrier values. Comparable experiments were subsequently performed with three unembalmed cadaver subjects using the same headform and test procedures.

A comparison of the NHTSA advanced dummy and the Hybrid III is presented in this paper based on their performance in repeated sled tests under 3 different restraint systems. The restraint systems considered are: the airbag alone, the 3-point belt alone, and a combined use of the airbag and the 3-point belt. Various time-histories pertaining to accelerations, angular velocities, deflections and forces have been compared between the two dummies in order to study their repeatability. The Hybrid III appears to be more repeatable than the NHTSA advanced dummy in its response in one case, that of restraint with the 3-point belt alone. The response of the NHTSA advanced dummy in other two restraint modes, the airbag alone and the combination of 3-point belt and airbag, appears to be no less repeatable than that of Hybrid III in this series of tests.

A new approach for improving estimates of the kinematic response of ATDs (anthropomorphic test devices) to vehicle crash events has been developed. This approach employs the Kalman Filter; a state model based estimation approach that has been widely applied to system dynamics problems ranging from navigation to missile guidance. The Kalman Filter approach combines measurements of crash event phenomena (acceleration and displacement), kinematic models of ATD behavior and statistics of sensor noise to create precise estimates of ATD motion during a crash. This paper presents an implementation of a state model and Kalman Filter for a sensor data collected from the chest of an ATD during an out-of-position airbag deployment test. Favorable comparisons are made between the Kalman Filter model approach and traditional methods involving numerical integration and differentiation.

One method of understanding the general mechanical response of a complex system such as a vehicle, a human surrogate, a bridge, a boat, a plane, etc., is to subject it to an input, such as an impact, and obtain the response time-histories. The responses can be accelerations, velocities, strains, etc. In general, when experiments of this type are run the responses are contaminated by sample-to-sample variation, test-to-test variability, random noise, instrumentation noise, and noise from unknown sources. One common method of addressing the noise in the system to obtain the underlying response is to run multiple tests on different samples that represent the same system and add them together obtaining an average. This functionally reduces the random noise. However, if the fundamental response of each sample is not the same, then it is not altogether clear what the average represents. It may not capture the underlying physics.